Particle filter based self sensing Shape Memory Alloy wire actuator under external cooling

Mohan, Sagar; Banerjee, Atanu

doi:10.1016/j.ymssp.2022.109779

Cited by 4 publications

(2 citation statements)

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“…This temperature generated within the wire results in phase transformation from martensite to austenite or vice versa, depending upon whether the wire is being heated up or cooled equations ( 2) and (3), the phase transformation results in strain generation within the wire. The strain generated within the wire then results in mechanical motion in the actuator equation (4), with the relation between phase transformation and strain generated shown in [16]. The aforementioned equations indicate complex hysteresis relationships that have been mathematically indicated; however, mathematical models involve approximations and may be true in certain sets of conditions.…”

Section: Modeling Of Sma Actuatormentioning

confidence: 99%

“…When the material experiences a temperature change, it results in an ensuing phase transformation from martensite to austenite, which causes strain recovery [3], which can further be utilized for mechanical actuation. The heat can be generated through convection joule heating, which makes the SMA actuator electronically controllable [4], i.e. controlled heating utilizing passage of electrical current through the SMA material can induce phase transformation, influencing the SME.…”

Section: Introductionmentioning

confidence: 99%

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A comparative analysis between deep neural network-based 1D-CNN and LSTM models to harness the self-sensing property of the shape memory alloy wire actuator for position estimation

Singh,

Bhargaw,

Jadhav

et al. 2024

Smart Mater. Struct.

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The article presents a performance based comparative analysis of popular deep neu-ral network(DNN) models such as 1D-CNN and LSTM for Shape Memory alloy(SMA)-based wire actuator position estimation. These DNN models utilize the self-sensingproperty(SSP) for position prediction of the SMA actuator. The phase dependent elec-trical resistivity of SMA wire act as SSP, where the electrical resistivity is in the formof resistance acts as inputs to the proposed models for precise estimation of currentposition of the SMA actuator. For effective position control of the actuator, an ac-curate sensor feedback is required, utilizing SSP results in the elimination of externalposition sensor. This will improve the overall system in terms of more compactness andreduced interface complexity. Coming to DNN models, 1-D CNN has been meagerlyexplored in the current literature landscape for self-sensing prediction of SMA actua-tors, these 1-D CNN models are becoming quite popular for time series prediction forvarious applications and are emerging as an alternative to LSTM models. In this paper,a novel implementation of a 1D-CNN model for SMA actuator position estimation hasbeen done. A comparative analysis between 1D-CNN and LSTM has been done forprediction capability and inference speed based on performance measures such as MeanSquare Error(MSE), Mean Absolute Error(MAE), sMAPE(symmetric Mean AbsolutePercentage Error), data distribution and average inference speed. This comparisonshows that 1D-CNN has matching performance with the LSTM model with respect tothe prediction capability, however 1D-CNN offers faster inference speed. This analy-sis can be useful for choosing suitable DNN model for deployment in low computingplatform such as micro-controller for SMA actuator based real time applications wheretime latency is a critical parameter.

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Section: Modeling Of Sma Actuatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%